Terrestrial bioacoustics, like many other domains, has recently
witnessed some transformative results from the application of deep
learning and big data (Stowell 2017, Mac Aodha et al. 2018, Fairbrass et
al. 2018, Mercado III and Sturdy 2017). Generalising over specific
projects, which bioacoustic tasks can we consider "solved"? What can we
expect in the near future, and what remains hard to do? What does a
bioacoustician need to understand about deep learning? I will address
these questions, giving a concise summary of recent developments and
ways forward. We build on recent projects and evaluation campaigns led
by the author (Stowell et al. 2015, Stowell et al. 2018), as well as
broader developments in signal processing, machine learning and
bioacoustic applications of these. We will discuss which type of deep
learning networks are appropriate for audio data, how to address
zoological/ecological applications which often have few available data,
and issues in integrating deep learning predictions with existing
workflows in statistical ecology.